CN107506331A - Micro-capacitance sensor reliability calculation method based on temporal associativity and element run time - Google Patents
Micro-capacitance sensor reliability calculation method based on temporal associativity and element run time Download PDFInfo
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Abstract
A kind of micro-capacitance sensor reliability calculation method based on temporal associativity and element run time, this method is according to micro-capacitance sensor element run time, reference element tub curve obtains the accurate fault rate of element, by establishing moment label and season label, randomness, the fluctuation of new energy output are considered；Wind speed is extracted by improved Latin Hypercube Sampling method, intensity of illumination is determined by moment and season label, and then calculates new energy power supply output, state duration sampling is then based on and carries out Calculation of Reliability.This method is applied to the micro-capacitance sensor Calculation of Reliability containing new energy power supply, passage time label has got in touch with the new energy power supply output and load of any instant, and fault rate of the element under true running situation is considered, can more accurate, more realistically calculate the micro-capacitance sensor reliability of the power supply containing new energy.
Description
Technical field
The present invention relates to Power System Reliability to calculate research field, particularly, be related to it is a kind of based on temporal associativity and
The method that element run time is calculated the reliability of micro-capacitance sensor.
Background technology
Power industry is the basis of the national economic development, and power grid security is the important component of social public security.It is long
Since phase, thermal power generation is in leading position in China's power industry, a large amount of consumption heavy damages of traditional energy ecology
Environment simultaneously causes energy crisis.Develop generation of electricity by new energy based on wind energy, solar energy be built environment friendly society must
So selection.Traditional power network large-scale blackout happens occasionally, and the distributed generation technology to be generated electricity using new energy turns into new shape
The trend of Sustainable Development of Power Industry under gesture.
Distributed power generation has the advantages that pollution is small, network loss is small, flexible for installation, generally by it is small-scale it is distributed in the form of pacify
Near load point, effective supplement as bulk power grid can improve power network peak valley performance.But distributed power generation is more using once
The energy, power output is unstable, is a uncontrollable source for bulk power grid, in order to preferably solve distributed power source with it is big
Contradiction between power network, it is proposed that the concept of micro-capacitance sensor.
The development of micro-capacitance sensor is not only utilized new energy, reduces the loss that system blackout accident is brought again, its
Power grid construction investment has been saved in access, is added the flexibility of operation of power networks control, is improved load point power supply reliability.As
Multi-power system, when an error occurs micro-capacitance sensor can form local isolated island, isolated island internal loading can be continued to power by micro battery, now
The power output of micro battery determines the power supply reliability to load point, therefore traditional reliability estimation method can not be transported reasonably
Use in micro-capacitance sensor reliability assessment.Specifically, micro-capacitance sensor Calculation of Reliability is mainly influenceed by following factor：
1. micro-capacitance sensor structure.Micro-capacitance sensor structure is more complicated, and its Calculation of Reliability process is more complicated, and the calculating time is longer, uses
Family is influenceed by element fault, and possibility is bigger, and reliability index may be poorer.
2. element failure rate.Element failure rate is higher, and micro-capacitance sensor power off time caused by by element fault is longer, load point
Power off time is longer, and reliability index is poorer.
3. new energy power supply is contributed.New energy power supply is contributed bigger (generated energy of namely new energy is bigger), it is general and
Say that micro-capacitance sensor reliability is stronger, but the load of the moment all load points might not be disclosure satisfy that in each time, new energy
Value.
4. payload.Load value is needed compared with new energy power supply power generating value in micro-capacitance sensor, when load value is more than
During new energy gross capability value, sub-load needs to cut off.
5. reliability calculation method.Have analytic method and the class of simulation two in micro-capacitance sensor Calculation of Reliability, analytic method use compared with
Carry out the reliability of computing system for strict mathematical modeling and effective algorithm, although with the higher degree of accuracy, should
Method can constantly increase amount of calculation with the increase of system element number, and the consideration to enchancement factor is not in place, therefore more suitable
Few for component number, failure ratio is sparser, but has the single system of significant impact.Simulation is primarily referred to as Monte Carlo mould
Plan method, this method, suitable for multidimensional, higher-dimension problem and larger system scale, have convenient simulation based on probability statistics
The distribution of non-exponential type, the probability-distribution function or numerical characteristic that can contemplate engineering practice, reliability index can be obtained
The advantages that.
The Generation System Reliability for new energy has carried out certain research in the prior art, but the studies above method is equal
Have the following disadvantages：
1. research is not yet related to the temporal associativity that new energy is contributed with load, simulated to any instant system mode
When, new energy power supply is not contributed exactly and corresponded with load value.
2. not considering existing element run time in Calculation of Reliability, the failure of element useful life phase is used without exception
Certain error be present in rate.
3. when being sampled to wind speed, the tradition methods of sampling is difficult to travel through whole sections and the sample mode has larger error.
The content of the invention
It is an object of the invention to propose a kind of micro-capacitance sensor reliability meter based on temporal associativity and element run time
Calculation method, passage time label have got in touch with the new energy power supply output (i.e. generated energy) and load of any instant, and consider
Fault rate of the element under true running situation, the micro-capacitance sensor that can more accurate, more realistically calculate the power supply containing new energy are reliable
Property.
To use following technical scheme up to this purpose, the present invention：
A kind of micro-capacitance sensor reliability calculation method based on temporal associativity and element run time, comprises the following steps：
Step 110：The structure of micro-capacitance sensor is determined, determines the original state of element, according to the run time of element, obtains member
The fault rate of part；
Step 120：The duration sampling of current state is rested on to each element, simulates the element in period T
Running status duration time sequence, and synthesis obtains status switch and the duration of system；
Step 130：System mode sequence is examined in, for each fault moment, the moment is occurred according to failure
The integral point moment closed on is found, time tag t is obtained and season label S is determined according to the moment；
Step S140：For each fault moment, according to obtained time tag t and season label S, with reference to new energy
Seasonal variations trend, the climatic condition of new energy is obtained, new energy, which is calculated, according to the climatic condition contributes, according to described
New energy is contributed and combines micro-capacitance sensor loading condition, and calculating analysis is carried out to the system failure moment；
Step 150：According to the result for the system failure moment calculate analysis, obtain reacting each load point of micro-capacitance sensor
Reliability index, and utilize load point reliability index computing system reliability index.
Optionally, in step 110, element includes breaker, feeder line and transformer, first according to the operation of each element
Time determines which period each element is in, and then determines that element failure rate, the fault rate include according to tub curve
Phase and the fault rate in senescence phase.
Optionally, the senescence phase fault rate of the transformer obeys such as minor function：
Feeder line senescence phase fault rate obeys such as minor function：
Wherein x1∈ [21.25,25], x2∈ [52.08,60].
Optionally, step 120 specifically comprises the following steps：
Step 121, the original state of element is determined, usually assumes that all element initial times are in running status；
Step 122, the duration that current state is rested on to each element is sampled, for recoverable two state
Element, including transformer, feeder line and breaker, according to the fault rate λ and repair rate μ of element, met using equation below
The time between failures τ of exponential distribution1With fault correction time τ2：
Wherein, G1And G2It is the uniform random number on [0,1]；
Step 123, according to the time between failures τ of element1With fault correction time τ2, it is total to simulate given simulation
Element running status duration time sequence in period T；
Step 124, the running status duration time sequence of comprehensive all elements, the system mode sequence of whole micro-capacitance sensor is obtained
Row and duration.
Optionally, in step S140, the new energy includes wind-force and solar energy, is marked according to time tag t and season
S is signed, it is determined that corresponding wind speed probability density curve and intensity of illumination curve, sampling is used for wind speed probability density curve
Wind speed is determined, intensity of illumination is determined by time tag t and season label S for intensity of illumination curve, obtains wind speed, intensity of illumination
After numerical value, determine that blower fan, photovoltaic are contributed, contributed according to blower fan, photovoltaic and combine micro-capacitance sensor loading condition, the system failure moment is entered
Row calculates analysis.
Optionally, the wind speed is determined using improved Latin hypercube.
Optionally, the sampling point selection of improved Latin Hypercube Sampling method is：
Wherein, N is frequency in sampling, 1≤n≤N.x1, x2…xKFor K stochastic variable, E (xk) represent x sequences expectation
Value, wherein xkThe cumulative distribution function of (1≤k≤K) is Yk=Fk(xk), the span [0,1] of this distribution function is divided into
N number of subinterval, Y-1Represent the inverse function of the cumulative distribution function.
Optionally, step 130 and step 140 circulation are carried out, until malfunction is last failure in time end T
State, just carry out step 150.
Optionally, the reliability index of each load point is year fault rate λi, annual stop transport duration Ui, with
Average stoppage in transit duration γ having a power failure every timei；
The Reliability Index includes：
(1) system System average interruption frequency index S AIFI, unit：Secondary/(family year)
Wherein, λiAnd NiRespectively load point i fault rate and number of users；R is the set of all load points of system；
(2) system System average interruption duration index S AIDI, unit：Hour/(family year)
Wherein, UiStopped transport the duration for load point i annual, unit：Hour/year；
(3) user's System average interruption duration index CAIDI, unit：Hour/(customer interrupted year)
(4) averagely power Availability Index ASAI
(5) not enough power supply index ENS, kilowatt-hour/year
Wherein, LaiTo be connected to load point i average load, unit：kW；UiContinue for load point i annual stoppage in transit
Time, unit：Hour/year.
The invention also discloses a kind of storage medium, for storing computer executable instructions,
The computer executable instructions perform above-mentioned method when being executed by processor.
The present invention has advantages below：
1. in micro-capacitance sensor Calculation of Reliability, obtain considered when new energy power supply is contributed new energy contribute with load when
Between relevance, by the relation in wind speed, intensity of illumination and season, moment, it is bent to establish wind speed, moment in the season distribution of intensity of illumination
Line, it is intended that accurate new energy is obtained during Calculation of Reliability and is contributed.
2. when obtaining element failure rate, it is contemplated that the run time of element time of running element, in existing micro-capacitance sensor
Element is not in the useful life phase, therefore its accurate fault rate depends on the time that it has run.
3. after season and moment label is determined, employs improved Latin Hypercube Sampling method and obtain wind speed, should
Method can not only realize the stratified sampling between the whole district, also take into account the desired value of data so that the sampling to wind speed is more complete
Face is accurate.
Brief description of the drawings
Fig. 1 is the step flow chart according to the micro-capacitance sensor reliability calculation method of the specific embodiment of the invention；
Fig. 2 is the micro-capacitance sensor schematic diagram according to the power supply containing new energy of the specific embodiment of the invention；
Fig. 3 is the tub curve schematic diagram according to the transformer of the specific embodiment of the invention；
Fig. 4 is the tub curve schematic diagram according to the feeder line of the specific embodiment of the invention；
Fig. 5 is state duration Sampling schematic diagram；
Fig. 6 is 24 four seasons at the moment total load curve synoptic diagrams according to the specific embodiment of the invention；
Fig. 7 is in certain season wind speed probability density curve according to the specific embodiment of the invention；
Fig. 8 is in certain moment intensity of illumination curve according to the specific embodiment of the invention；
Fig. 9 is improved Latin Hypercube Sampling method sample point contrast schematic diagram.
Embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining the present invention, rather than limitation of the invention.It also should be noted that in order to just
Part related to the present invention rather than entire infrastructure are illustrate only in description, accompanying drawing.
In the present invention, micro-capacitance sensor refers to by distributed power source, energy storage device, energy conversion device, load, monitoring and protection
The small-sized electric system of the compositions such as device, micro-capacitance sensor reliability, that is, refer to that the power distribution network of the power supply containing distributed new is lonely
State when island is run.Seek to be directed to any one micro-capacitance sensor, especially autonomous micro-capacitance sensor, be exactly commonly connected with bulk power grid
Point disconnect micro-capacitance sensor, calculate its reliability, distributed power source contained in this micro-capacitance sensor, these distributed power sources using wind,
The new energy power supply such as light, final purpose is to calculate the numerical value of reliability index, and is analyzed.
" output --- duration of load application label " is established in the invention, considers randomness, the fluctuation of generation of electricity by new energy output
Property, according to micro-capacitance sensor element run time, reference element tub curve obtains the fault rate of the element mainly easily to break down,
By handling the historical data of wind speed, intensity of illumination, wind speed probability density curve, intensity of illumination curve are fitted, is led to
Cross improved Latin Hypercube Sampling method and extract wind speed, intensity of illumination is determined by moment and season label, and then calculate
New energy power supply is contributed, and is finally based on state duration sampling and is carried out Calculation of Reliability.
This method is applied to contain generation of electricity by new energy micro-capacitance sensor Calculation of Reliability, and passage time label has got in touch with any instant
Generation of electricity by new energy output and load, can more accurate, more realistically calculate the micro-capacitance sensor reliability of the power supply containing new energy.
Referring to Fig. 1, the step flow chart of the micro-capacitance sensor reliability calculation method according to the specific embodiment of the invention is shown,
Comprise the following steps：
Step 110：The structure of micro-capacitance sensor is determined, determines the original state of element, according to the run time of element, obtains member
The fault rate of part；
Exemplary, referring to Fig. 2, a kind of exemplary micro-capacitance sensor structure is shown, the micro-capacitance sensor is mainly sent out by new energy
Electric equipment, energy storage device composition, exemplary new energy equipment include wind power generating set, photovoltaic battery panel, and energy storage is set
It is standby to include lithium battery, ultracapacitor etc..Specifically, it is including feeder line two, breaker ten, distribution transformer eight, negative
Lotus point eight, load species include residential electricity consumption, commercial power and commercial power, and various types of load proportion is stable, each load
Total load ratio is stable shared by point.
And in this step, element generally refers to the element easily to break down, can be breaker, feeder line and transformation
Device.
The fault rate of element is an important parameter.Table is studied to the fault data of a large amount of different type elements
Bright, the bathtub curve of element is in bathtub shapes, is represented with tub curve, indicates failure of the element in whole life cycle
Rate changes.It can be divided into initial stage, mid-term and senescence phase according to time sequential.
It is also known as debugger initial stage, will not generally puts into operation, element is early stage coming into operation, due to the defects of designing
Or the imperfect of manufacturing process is exposed quickly, thus fault rate is higher.Through debugging after a while, fault rate is with the time
Increase and be gradually reduced, tend towards stability, i.e., into the useful life phase.Mid-term is also known as useful life phase or random failure period.
By the debugging of previous stage, the fault rate of element is low and steady, is approximately a constant.The generation of failure is only because accidentally
The reason for, probability distribution at this moment is typically considered exponential distribution.Senescence phase is also known as the attenuation phase.During this period, element due to
The reasons such as aging, fatigue and abrasion and enter ageing phase, the fault rate in this period rises rapidly with the growth of time, can
With Weibull distribution, just too distribution, gamma distribution etc. describe.
Further, in the reliability of computing element of the present invention, determined first according to the run time of each element each
Which period individual element is in, and then determines element failure rate according to tub curve, and the fault rate includes mid-term and senescence phase
Fault rate, wherein mid-term is steady state value, and the later stage is exponential function.The fault rate of wherein breaker is represented with access times.Show
Example property, the mid-term reliability of element is shown in table 1.
Table 1：The mid-term reliability of the element mainly to break down
Referring to Fig. 3, Fig. 4, respectively illustrate the tub curve schematic diagram of transformer and feeder line.
Further, the senescence phase fault rate of the transformer obeys such as minor function：
Feeder line senescence phase fault rate obeys such as minor function：
Wherein x1∈ [21.25,25], x2∈ [52.08,60].
Step 120：The duration sampling of current state is rested on to each element, simulates the element in period T
Running status duration time sequence, and synthesis obtains status switch and the duration of system.
For step 120, obtained using " state duration sampling ".State duration sampling is a kind of sequential
Monte Carlo method.State duration sampling is sampled based on the probability distribution to the element state duration.It is assumed that
Element run time and malfunction lower repair time obey certain probability distribution (such as exponential distribution), then according to element
Fault rate and repair rate sampling determine state and state duration of the element in preset time section.When in preset time section
After the state and state duration of all elements determine, it is possible to obtain state and the duration of system.Specifically, walk
Rapid 120 comprise the following steps：
Step 121, the original state of element is determined, usually assumes that all element initial times are in running status；
Step 122, the duration that current state is rested on to each element is sampled, for recoverable two state
Element, including transformer, feeder line and breaker, according to the fault rate λ (mean time between failures MTTF inverse) of element
With repair rate μ (mean repair time MTTR inverse), using transform method obtain meet the no-failure operation of exponential distribution when
Between τ1With fault correction time τ2：
Wherein, G1And G2It is the uniform random number on [0,1].
Step 123, according to the time between failures τ of element1With fault correction time τ2, it is total to simulate given simulation
Element running status duration time sequence in period T；
Step 124, the running status duration time sequence of comprehensive all elements, the system shape of whole micro-capacitance sensor can be obtained
State sequence and duration, in each system mode, each element state is constant.
Exemplary, referring to Fig. 5, state duration Sampling schematic diagram is shown, in the figure, abscissa represents
Duration, ordinate represent state.
The operation to 3 elements (A, B and C) and malfunction continuous-time analog are first passed through, then obtains system mode
And state duration, give in simulated time section in figure and simulate 11 system modes altogether.
The period T is the time for simulate calculating, exemplary, can be 10 years, 20 years, 50 years, 100 years etc.
The time commonly used in electric network reliability is investigated.
And status switch and the duration of the system according to shown by the figure, it can obtain not considering new energy to this
Micro-capacitance sensor provides the micro-capacitance sensor principal states parameter in the case of load.But micro-capacitance sensor itself has new energy equipment, i.e.,
Just power outage is produced, micro-capacitance sensor still possesses certain trend, and each new energy power supply, which is contributed, can feed certain load so that
The load normal operation of this micro-capacitance sensor, only when undercapacity is to supply the load of this power network, new failure may be caused to bear
Lotus point, i.e., contributed according to the new energy and combine micro-capacitance sensor loading condition, calculating analysis is carried out to the system failure moment, considered
The new energy of fault moment is contributed, the situation of load point load, and then determines electric power thus supplied.Therefore, it is also desirable to new energy is existed
Output situation at the time of fault moment carries out examining computation, and the climate characteristic of new energy be with it is constantly associated.Need
At the time of for failure, the output of new energy is estimated using the situation of wind speed, solar energy etc..So as to obtain the steps 130
With step 140.
Step 130：System mode sequence is examined in, for each fault moment, the moment is occurred according to failure
The integral point moment (least bit reduction was the upper integral point moment) closed on is found, time tag t is obtained and season is determined according to the moment
Label S；
In this step, by statistics limitation and amount of calculation are limited, according to principle nearby, fault moment is with close
The integral point moment is calculated, the morning 10：29 are considered at 10 points according to nearby principle.So-called least bit reduction to a upper integral point,
I.e. 10：30 fault moment, by 10 points of calculation, and according to moment and season label, wind-force and solar energy situation can be obtained, from
And obtain corresponding wind-power electricity generation value and solar power generation value.
Step S140：For each fault moment, according to obtained time tag t and season label S, with reference to new energy
Seasonal variations trend, the climatic condition of new energy is obtained, new energy, which is calculated, according to the climatic condition contributes, according to described
New energy is contributed and combines micro-capacitance sensor loading condition, and calculating analysis is carried out to the system failure moment.
Specifically, the new energy includes wind-force and solar energy, according to time tag t and season label S, it is determined that corresponding
Wind speed probability density curve and intensity of illumination curve, wind speed is determined using sampling for wind speed probability density curve, it is right
Intensity of illumination is determined by time tag t and season label S in intensity of illumination curve, after obtaining wind speed, intensity of illumination numerical value, it is determined that
Blower fan, photovoltaic are contributed, and are contributed according to blower fan, photovoltaic and are combined micro-capacitance sensor loading condition, calculating analysis is carried out to the system failure moment.
Exemplary, when for solar energy, basis such as Fig. 8 that can be exemplary certain moment intensity of illumination curve, and
According to it is specific at the time of obtain intensity of illumination numerical value.For wind-force, when counting wind speed, the moment in the four seasons 24 is obtained by statistics constantly
Wind speed probability density curve, Fig. 7 shown according to the specific embodiment of the invention in certain season wind speed probability density curve, such as
For 10 points of winter.Wind speed is then obtained using the method for sampling, and then obtains wind-force and solar energy output situation.
When calculating micro-capacitance sensor loading condition, 24 four seasons at moment total load curve synoptic diagrams shown in Fig. 6, profit can be utilized
Load needed for this micro-capacitance sensor is obtained with time tag t and season label S, and according to wind-force and solar energy output situation, is analyzed
The new energy of fault moment is contributed, the situation of load point load, and then determines electric power thus supplied, and then the system failure moment is carried out
Analysis is calculated, is thus established " temporal associativity that new energy power supply is contributed with load ".
Specifically, when fault moment 10, by inquiring about 10 points of intensity of illumination curve, can be obtained with reference to season label
Intensity of illumination is obtained, and the wind speed feature in somewhere can be obtained by inquiring about 10 wind speed probability density curves of the morning.Load is also right
Answer the position of 10 points of load curve.
Further, the wind speed is determined using improved Latin hypercube.
To be drawn and formed by historical data due to wind speed probability density curve, its data has complete authenticity, but in reality
In the sample calculation of border, the point of the individual discrete in real history data can produce some to whole probability density curve and it is expected partially
Move.Therefore, under the advantages that based on this feature between original method ensure that the sampling covering whole district, each subinterval uniform sampling,
Based on the desired influence of probability density distribution, a kind of consideration desired Latin Hypercube Sampling method of probability is proposed
(Expectation Latin Hypercube Sampling, ELHS).Improved Latin Hypercube Sampling method is ensureing original
Under conditions of some hypercube sampling, according to the desired value of variable, two parts are divided into.
[E(xk)] refer to the Left half-plane of probability density curve desired value；
[E(xk)] refer to the RHP of probability density curve desired value.
So that in sampling interval, the right margin in the selective sampling section of left plane, the left side in right plane selective sampling section
Boundary, both sides are drawn close to desired value simultaneously, by formulaRealize.
To sum up：The sampling point selection of improved Latin Hypercube Sampling method is：
Wherein, N is frequency in sampling, 1≤n≤N.x1, x2…xKFor K stochastic variable, E (xk) represent x sequences expectation
Value, wherein xkThe cumulative distribution function of (1≤k≤K) is Yk=Fk(xk), the span [0,1] of this distribution function is divided into
N number of subinterval, Y-1Represent the inverse function of the cumulative distribution function.
Referring to Fig. 9, the Latin Hypercube Sampling method (MLHS) of left figure and the improved Latin hypercube of right figure are shown
The difference of the methods of sampling (ELHS).It can be seen that the desired value E (x according to variablek) by curve be divided into left and right two portions
Point, under conditions of original Latin Hypercube Sampling is ensured, Left half-plane chooses its right margin, and RHP chooses its left side
Boundary, both sides are drawn close to desired value simultaneously.It is expected E (xk) both sides sample point A, B through ELHS methods improvement after be chosen to be a little
C。
Step 130 and step 140 circulation carry out, until malfunction be time end T in last malfunction,
Carry out step 150.
Step 150：According to the result for the system failure moment calculate analysis, obtain reacting each load point of micro-capacitance sensor
Reliability index, and utilize load point reliability index computing system reliability index.
Therefore, by update later system mode sequence can obtain final load point reliability index and because
This Reliability Index being calculated, so as to enter from each load point, and the aspect of system etc. two to the reliability of entirety
Row is weighed.
Further, the reliability index of each load point is year fault rate (load-point failurerate)
λi, annual stop transport duration (load-point annual unavailability) Ui, with the average stoppage in transit having a power failure every time
Duration (load-point outage duration) γi, computing system reliability index.
What load point reliability index characterized is the reliability level of each load point power supply in micro-capacitance sensor, and its reflection is
The probability level of micro-capacitance sensor reliability long-term average, rather than the value of a certain determination.Reliability Index is from system
Angle carries out comprehensive measurement to its reliability.The system-level reliability index of micro-grid system can export from load point index,
These indexs can be used for reflection in the past or the systematic function in future.
The Reliability Index includes：
(1) system System average interruption frequency index (system average interruption frequency index)
SAIFI, unit：Secondary/(family year)
Wherein, λiAnd NiRespectively load point i fault rate and number of users；R is the set of all load points of system；
(2) system System average interruption duration index (system average interruption duration
Index) SAIDI, unit：Hour/(family year)
Wherein, UiStopped transport the duration for load point i annual, unit：Hour/year.
(3) user's System average interruption duration index (customer average interruption duration
Index) CAIDI, unit：Hour/(customer interrupted year)
(4) averagely power Availability Index (average service availability index) ASAI
(5) not enough power supply index (energy not supplied) ENS, kilowatt-hour/year
Wherein, LaiTo be connected to load point i average load, unit：kW；UiContinue for load point i annual stoppage in transit
Time, unit：Hour/year.The average load L of systemaCan be according to system requirements LpLoad coefficient f is multiplied by obtain, or according to
Load duration curve calculates the total electricity needed for institute search time, then divided by time of research obtain, finally by one
The average load L of load point is calculated in fixed pro rate canai。
Embodiment 1：
By taking southern city (119 ° 33 ' 19 " E, 23 ° 34 ' 02 " N) certain micro-capacitance sensor as an example, the present invention is made further
It is bright.
A. the micro-capacitance sensor is made up of wind power generating set, photovoltaic battery panel, energy storage device etc., includes feeder line two, open circuit
Device ten, distribution transformer eight, load point eight, load species include residential electricity consumption, commercial power and commercial power, respectively
Species load proportion is stable, and total load ratio shared by each load point is stable；
B. blower fan 300 is contained in the microgrid, photovoltaic cells 5000, diesel engine 10, lithium battery 1.5 × 105 is super
60, capacitor；
C. blower fan rated power 30kW, rated wind speed 12m/s, wind speed 3m/s, cut-out wind speed 24m/s, photovoltaic cells are cut
Rated power 0.2kW, standard intensity of illumination 1kW/m2,25 DEG C of standard operating temperature, temperature power coefficient -0.0045, diesel engine
Rated power 100kW, lithium battery unit capacity 3.2v 3000mAH, ultracapacitor unit capacity 1kWh；
D. equipment initial stage is limber up period, will not be put into operation；Equipment mid-term is the useful life phase, and fault rate is steady state value；
Equipment senescence phase fault rate changes and changed with run time, and equipment is changed when reaching lifetime limitation；
E. transformer, the run time of feeder line are 22.5,11.7 respectively, and transformer senescence phase fault rate obeys functionFeeder line senescence phase fault rate obeys functionWherein x1∈
[21.25,25], x2∈[52.08,60]；
F. total load ratio shared by load point 1-8 is respectively 15%, 8%, 27%, 20%, 4%, 11%, 9%, 6%, bag
It is respectively 1,40,120,2,18,1,45,23 containing number of users, region load maximum is 1.7MW；
G. simulation time T takes 100 years, wind speed, intensity of illumination data reference network address：
http：//www.cwb.gov.tw/V7/index.htm, calculate reliability index it is as shown in the table：
The load point reliability index result of calculation of table 2
The Reliability Index result of calculation of table 3
| Reliability index | Result of calculation |
| SAIFI (secondary/family year) | 0.4805 |
| SAIDI (hour/family year) | 5.6911 |
| CAIDI (hour/stop family year) | 11.8441 |
| ASAI | 0.9994 |
| ENS (MWh/) | 9.7687 |
The present invention further discloses a kind of storage medium, and for storing computer executable instructions, the computer can
Execute instruction performs above-mentioned method when being executed by processor.
The present invention has advantages below：
1. in micro-capacitance sensor Calculation of Reliability, obtain considered when new energy power supply is contributed new energy contribute with load when
Between relevance, by the relation in wind speed, intensity of illumination and season, moment, it is bent to establish wind speed, moment in the season distribution of intensity of illumination
Line, it is intended that accurate new energy is obtained during Calculation of Reliability and is contributed.
2. when obtaining element failure rate, it is contemplated that the run time of element time of running element, in existing micro-capacitance sensor
Element is not in the useful life phase, therefore its accurate fault rate depends on the time that it has run.
3. after season and moment label is determined, employs improved Latin Hypercube Sampling method and obtain wind speed, should
Method can not only realize the stratified sampling between the whole district, also take into account the desired value of data so that the sampling to wind speed is more complete
Face is accurate.
Obviously, it will be understood by those skilled in the art that above-mentioned each unit of the invention or each step can be with general
Computing device realizes that they can be concentrated on single computing device, alternatively, they can be can perform with computer installation
Program code realize, so as to being stored in storage device by computing device to perform, or by they point
Each integrated circuit modules are not fabricated to, or the multiple modules or step in them are fabricated to single integrated circuit module
Realize.So, the present invention is not restricted to the combination of any specific hardware and software.
Above content is to combine specific preferred embodiment further description made for the present invention, it is impossible to is assert
The embodiment of the present invention is only limitted to this, for general technical staff of the technical field of the invention, is not taking off
On the premise of from present inventive concept, some simple deduction or replace can also be made, should all be considered as belonging to the present invention by institute
Claims of submission determine protection domain.
Claims (10)
1. a kind of micro-capacitance sensor reliability calculation method based on temporal associativity and element run time, comprises the following steps：
Step 110：The structure of micro-capacitance sensor is determined, determines the original state of element, according to the run time of element, obtains element
Fault rate；
Step 120：The duration sampling of current state is rested on to each element, simulates the element operation in period T
State duration sequence, and synthesis obtains status switch and the duration of system；
Step 130：System mode sequence is examined in, for each fault moment, found according to the failure generation moment
At the integral point moment closed on, obtain time tag t and season label s is determined according to the moment；
Step S140：For each fault moment, according to obtained time tag t and season label s, with reference to new energy season
Variation tendency, the climatic condition of new energy is obtained, new energy, which is calculated, according to the climatic condition contributes, according to the new energy
Source is contributed and combines micro-capacitance sensor loading condition, and calculating analysis is carried out to the system failure moment；
Step 150：According to the result that analysis was calculated the system failure moment, obtain the reliability of the reaction each load point of micro-capacitance sensor
Index, and utilize load point reliability index computing system reliability index.
2. micro-capacitance sensor reliability calculation method according to claim 1, it is characterised in that：
In step 110, element includes breaker, feeder line and transformer, is determined first according to the run time of each element each
Which period individual element is in, and then determines element failure rate according to tub curve, and the fault rate includes mid-term and senescence phase
Fault rate.
3. micro-capacitance sensor reliability calculation method according to claim 2, it is characterised in that：
The senescence phase fault rate of the transformer obeys such as minor function：
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Feeder line senescence phase fault rate obeys such as minor function：
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Wherein x1∈ [21.25,25], x2∈ [52.08,60].
4. micro-capacitance sensor reliability calculation method according to claim 1, it is characterised in that：
Step 120 specifically comprises the following steps：
Step 121, the original state of element is determined, usually assumes that all element initial times are in running status；
Step 122, the duration that current state is rested on to each element is sampled, for recoverable two state member
Part, including transformer, feeder line and breaker, according to the fault rate λ and repair rate μ of element, satisfaction is obtained using equation below and referred to
The time between failures τ of number distribution1With fault correction time τ2：
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Wherein, G1And G2It is the uniform random number on [0,1]；
Step 123, according to the time between failures τ of element1With fault correction time τ2, simulate given simulation total time
Element running status duration time sequence in section T；
Step 124, the running status duration time sequence of comprehensive all elements, obtain whole micro-capacitance sensor system mode sequence and
Duration.
5. micro-capacitance sensor reliability calculation method according to claim 1, it is characterised in that：
In step S140, the new energy includes wind-force and solar energy, according to time tag t and season label s, it is determined that corresponding
Wind speed probability density curve and intensity of illumination curve, wind speed is determined using sampling for wind speed probability density curve, it is right
Intensity of illumination is determined by time tag t and season label s in intensity of illumination curve, after obtaining wind speed, intensity of illumination numerical value, it is determined that
Blower fan, photovoltaic are contributed, and are contributed according to blower fan, photovoltaic and are combined micro-capacitance sensor loading condition, calculate the system failure moment knot of analysis
Fruit.
6. micro-capacitance sensor reliability calculation method according to claim 5, it is characterised in that：
The wind speed is determined using improved Latin hypercube.
7. micro-capacitance sensor reliability calculation method according to claim 6, it is characterised in that：
The sampling point selection of improved Latin Hypercube Sampling method is：
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Wherein, N is frequency in sampling, 1≤n≤N.x1, x2…xKFor K stochastic variable, E (xk) represent x sequences desired value, wherein
xkThe cumulative distribution function of (1≤k≤K) is Yk=Fk(xk), the span [0,1] of this distribution function is divided into N number of sub-district
Between, Y-1Represent the inverse function of the cumulative distribution function.
8. micro-capacitance sensor reliability calculation method according to claim 1, it is characterised in that：
Step 130 and step 140 circulation carry out, until malfunction be time end T in last malfunction, just progress
Step 150.
9. the micro-capacitance sensor reliability calculation method according to claim 1 or 8, it is characterised in that：
The reliability index of each load point is year fault rate λi, annual stop transport duration Ui, it is flat with having a power failure every time
Stop transport duration γi；
The Reliability Index includes：
(1) system System average interruption frequency index S AIFI, unit：Secondary/(family year)
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Wherein, λiAnd NiRespectively load point i fault rate and number of users；R is the set of all load points of system；
(2) system System average interruption duration index S AIDI, unit：Hour/(family year)
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Wherein, UiStopped transport the duration for load point i annual, unit：Hour/year；
(3) user's System average interruption duration index CAIDI, unit：Hour/(customer interrupted year)
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10. a kind of storage medium, for storing computer executable instructions,
Computer executable instructions perform claim when being executed by processor requires the method described in any one in 1-9.
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| CN111008793A (en) * | 2019-12-24 | 2020-04-14 | 天津相和电气科技有限公司 | User experience-oriented electricity-gas-heat comprehensive energy utilization reliability assessment method |
| CN111008793B (en) * | 2019-12-24 | 2023-10-31 | 天津相和电气科技有限公司 | User experience-oriented electric-gas-heat comprehensive energy reliability assessment method |
| CN111861259A (en) * | 2020-07-30 | 2020-10-30 | 中国大唐集团科学技术研究院有限公司华东电力试验研究院 | Load modeling method, system and storage medium considering time sequence |
| CN111861259B (en) * | 2020-07-30 | 2022-07-15 | 中国大唐集团科学技术研究院有限公司华东电力试验研究院 | Load modeling method, system and storage medium considering time sequence |
| CN115860586A (en) * | 2023-03-01 | 2023-03-28 | 英迪格(天津)电气有限公司 | Analysis system for railway power transformation and distribution faults |
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